Authors: Chuan He, Anh H. Le, Iris Z. Wang
Affiliation: Roswell Park Comprehensive Cancer Center, Cedars-Sinai
Abstract Preview: Purpose: To develop a non-measured and DVH-based (NMDB) IMRT QA framework integrating machine learning (ML) to classify lung SBRT VMAT plans prone to delivery errors
Methods: 560 Eclipse AcurosXB l...
Authors: Yabo Fu, Yang Lei, Yu Lei, Haibo Lin, Ruirui Liu, Tian Liu, Kenneth Rosenzweig, Charles B. Simone, Shouyi Wei, Jiahan Zhang
Affiliation: Icahn School of Medicine at Mount Sinai, University of Nebraska Medical Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York Proton Center
Abstract Preview: Purpose: Beam orientation optimization (BOO) in intensity-modulated radiation therapy (IMRT) is traditionally a complex, non-convex problem tackled with heuristic methods. This study benchmarks global...
Authors: Yang Sheng, Qingrong Jackie Wu, Qiuwen Wu, Xin Wu, Dongrong Yang
Affiliation: Duke University Medical Center
Abstract Preview: Purpose: Convergence speed is crucial for an optimizer. Faster convergence leads to better solutions with fewer iterations and less time. Recently, a machine learning (ML)-assisted framework employing...
Authors: Grant Evans, Maxwell Arthur Kassel, Charles Shang, Michael H. Shang, Stephen Shang, Timothy R Williams
Affiliation: South Florida Proton Therapy Institute, SFPRF, Department of Radiation Medicine, MedStar Georgetown University Hospital
Abstract Preview: Purpose:
Daily image guidance for head and neck intensity-modulated proton therapy (IMPT) presents significant challenges due to large target volumes and anatomical changes. Geometric deviations al...
Authors: Aliasghar Rohani, Rui Zhang
Affiliation: Louisiana State University, Baton Rouge, Louisiana, Department of Radiation Oncology, Mary Bird Perkins Cancer Center
Abstract Preview: Purpose: This study aimed to evaluate the impact of simultaneous optimization of multi-photon beam energy and fluence on IMRT and VMAT treatment planning.
Methods: An Elekta linear accelerator (lin...
Authors: Yuzhen Ding, Hongying Feng, Jason Michael Holmes, Baoxin Li, Wei Liu, Daniel Ma, Lisa McGee, Samir H. Patel, Jean Claude M. Rwigema, Sujay A. Vora
Affiliation: Arizona State University, Department of Radiation Oncology, Mayo Clinic, Mayo Clinic Arizona, Mayo Clinic
Abstract Preview: Purpose:
Intensity-modulated proton therapy (IMPT) is a preferred treatment modality for head and neck (H&N) cancer patients, offering precise tumor targeting while sparing surrounding organs at ri...
Authors: Manju Liu, Weiwei Sang, Yanyan Shi, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Lihua Zhang, Rihui Zhang
Affiliation: Jiahui International Hospital, Jiahui International Hospital, Radiation Oncology, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose: This study aims to transform cone-beam computed tomography (CBCT) images acquired from deep inspiration breath-hold (DIBH) breast cancer patients into high-fidelity synthetic CT (sCT) images....
Authors: Yang Sheng, Qingrong Jackie Wu, Qiuwen Wu, Xin Wu, Dongrong Yang
Affiliation: Duke University Medical Center
Abstract Preview: Purpose:
This study aims to leverage large language model (LLMs) to develop a human-in-the-loop agentic framework, enhancing the efficiency of treatment planning in radiotherapy.
Methods:
A L...
Authors: Qihui Lyu, Dan Ruan, Ke Sheng, Jingjie Yu
Affiliation: Department of Radiation Oncology, University of California, Los Angeles, University of California, San Francisco, Department of Radiation Oncology, University of California, San Francisco
Abstract Preview: Purpose: The robotic arm radiotherapy platform enables flexible delivery of non-coplanar and non-isocentric radiotherapy with variable Source-to-Isocenter Distances (SIDs). However, the high degrees o...
Authors: Martin Frank, Oliver Jรคkel, Niklas Wahl
Affiliation: Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Karlsruhe Institute of Technology (KIT)
Abstract Preview: Purpose: Machine learning (ML) models on normal tissue complication and tumor control probability ((N)TCP) exploiting e.g. dosiomic and radiomic features are playing an increasingly important role in ...
Authors: Malvern Madondo, Mark McDonald, Zhen Tian, Christopher Valdes, Ralph Weichselbaum, Xiaofeng Yang, David Yu, Jun Zhou
Affiliation: Department of Radiation & Cellular Oncology, University of Chicago, University of Chicago, Emory University, Department of Radiology, University of Chicago, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: Head-and-neck (HN) cancer patients often experience significant anatomical changes during treatment course. Proton therapy, particularly intensity-modulated proton therapy (IMPT), is sensitiv...
Authors: Wouter Crijns, Frederik Maes, Loes Vandenbroucke, Liesbeth Vandewinckele
Affiliation: Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven; Department of Radiation Oncology, UZ Leuven, Department ESAT/PSI, KU Leuven; Medical Imaging Research Center, UZ Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven
Abstract Preview: Purpose: To explore intentional deep overfit learning (IDOL) to exploit the initial treatment plan to predict an adaptive radiotherapy plan.
Methods: A conditional generative adversarial network is...
Authors: Yang Sheng, Qingrong Jackie Wu, Qiuwen Wu, Xin Wu, Dongrong Yang
Affiliation: Duke University Medical Center
Abstract Preview: Purpose: Gradient-based optimization is the standard approach for IMRT fluence map optimization (FMO). Recently, a machine learning (ML)-assisted framework using a one-layer neural network was propose...
Authors: Daisuke Kawahara, Takaaki Matsuura, Yuji Murakami, Ryunosuke Yanagida
Affiliation: Hiroshima High-Precision Radiotherapy Cancer Center, Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima
Abstract Preview: Purpose: In recent years, automation in radiation therapy planning using AI has gained significant attention to reduce the workload of treatment planners. Adaptive Radiation Therapy (ART), as a new fo...
Authors: Dante PI Capaldi, Lu Jiang, Qihui Lyu, Ke Sheng
Affiliation: Department of Radiation Oncology, University of California at San Francisco, Department of Radiation Oncology, University of California, San Francisco
Abstract Preview: Purpose:
Preclinical small animal studies help understand radiation-induced biological responses, toxicities, and mechanisms, facilitating the translation of new therapies to patient treatment. Int...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Zi Yang, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: Dose prediction (DP) is essential in guiding radiotherapy planning. However, current DP models for intensity-modulated radiation therapy (IMRT) primarily rely on fixed-beam orientations and a...
Authors: Min Geon Choi, Sean J. Domal, Ruiqi Li, Taoran Li, Mu-Han Lin, Yang Kyun Park, David D.M. Parsons, Justin D. Visak
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, UT Southwestern Medical Center, University of Texas Southwestern Medical Center, University of Pennsylvania, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Ethos X-ray-guided online adaptive radiotherapy (ART) enables precise, daily adaptive treatments but requires significant resources, limiting widespread adoption. Many treatment sites do not ...